Signal Processing for Joint Source -Channel Coding of Digital Images
Igor V. Kozintsev · 2000
This thesis addresses the problems of signal processing for image communication and restoration. Significant attention is devoted to developing novel stochastic models for images, investigating the information theoretic performance bounds for them, and designing efficient learning and inference methods for the proposed models. Unlike the commonly accepted approach in which the design of communication systems is performed by first compressing the data into binary representation and then channel coding it to recover from transmission errors, this thesis advocates the joint source-channel coding solution to the problem. The joint approach potentially leads to significant performance gains in emerging multiuser communication scenarios like digital audio and video broadcast (DAB and DVB) and multicast over wireless and wireline networks, multimedia communication in heterogeneous environments, and situations with uncertainty and fluctuations in the data source or channel parameters as is typic...